Spatial frequency filtering is essential to a variety of image processing applications. Spatial frequency filtering is currently being used for image enhancement in medical imagery, such as in Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), as an aid to detection of cancerous regions. In military applications, spatial frequency filtering is useful in the arena of target detection. In this case, bandpass filtering can be used to isolate features of a given dimension from an image, thus facilitating greatly the target detection algorithms.
Two of the existing methods for achieving spatial frequency filtering of images are computer software and analog discrete circuit implementations; both have significant drawbacks.
For the digital software approach, even a simple algorithm which produces "blurring" (low pass spatial frequency filtering) of an image can take up to several minutes to calculate, and is strongly dependent on the resolution of the image. For example, for a standard 1024.times.1024 grayscale image, a Gaussian blur of 20 pixels would take approximately 30 seconds using a standard Pentium computer; for higher resolution images with full color capability, this operation would take much longer. The power consumption for these systems is relatively high, with a standard desktop computer consuming in excess of 75 Watts.
Spatial frequency filtering has also been implemented [C. M. Mead, Analog VLSI and Neural Systems, 1989] in the analog domain using digital circuits comprising discrete circuits comprising field effect transistors (FETs). This method was shown to be effective for achieving local contrast control (high pass spatial frequency filtering). However, the discrete circuit approach has two fundamental drawbacks.
First, the lateral conductance between pixels is emulated by a complicated multiple FET circuit, which requires a large unit cell area ("real estate") for each pixel; typically dimensions of nearly 100 .mu.m.times.100 .mu.m are required. Because there is such a large real estate demand for this implementation, large format arrays (e.g. 1024.times.1024) cannot be fabricated, since the resultant array would have dimensions of 10 cm.times.10 cm. Current semiconductor processing techniques are not compatible with devices larger than 2 cm.
Second, like the computational algorithmic approaches to spatial frequency filtering, the discrete FET implementation also has a slow time response (.sup..about. 100 msec), in addition to large power requirements (&gt;20W for a 1024.times.1024), both of which are incompatible with real time remote sensing applications which require sub-millisecond response with sub-Watt power consumption.